Theory

Synthetic figures, ignoring baseline levels of each type.

2x2

library(tidyverse)
x2 <- read_csv(here::here("data", "2x2.csv"))
# 
# x2 %>% 
#   ggplot() +
#   aes(x = x, y = new, label = lab) +
#   geom_line(aes(y = baseline), linetype = 2, alpha = .5) + 
#   geom_line() +
#   geom_ribbon(aes(ymax = new, ymin = baseline), alpha = .2) + 
#   facet_wrap("outcome", nrow = 2) + 
#   geom_label() +  
#   geom_text(aes(label = lab2, y = baseline), check_overlap = T, hjust = 0, vjust = -.2) +
#   labs(y = "Change in Level Provided\n(e.g., the number of requests to agencies)",
#        title = "") + 
#   theme_void() + 
#   xlim(-.2,1.2) + 
#   ylim(-1.1,3.1) + 
#   theme(panel.border = element_rect(fill=NA),
#         axis.title.y = element_text(angle = 90))



x2 %>% 
  ggplot() +
  aes(x = x, y = new, label = lab) +
  geom_line(aes(y = baseline), linetype = 2, alpha = .5) + 
  geom_line() +
  geom_ribbon(aes(ymax = new, ymin = baseline), alpha = .2) + 
  facet_grid(priority ~ capacity) +
  geom_label() +  
  geom_text(y = 0, 
            label = "Baseline levels for new legislator\n(may differ between service and policy work)",
            aes(x = ifelse(capacity == "Capacity DOES affect behavior" & priority == "Shifting priorities DO affect behavior", 0.5, NA))) +
  labs(y = "Change in Level Provided\n(e.g., the number of requests to agencies)",
       title = "",
       x = "") + 
  #theme_minimal() + 
  xlim(-.2,1.2) + 
  ylim(-1.1,3.1) + 
  theme(panel.grid = element_blank(),
        panel.border = element_rect(fill=NA),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        panel.background = element_blank())

Divergent Predictions for the Change in Levels of Constituency Service and Policy Work as Legislators Gain Experience and Power

# x2 %>% 
#   ggplot() +
#   aes(x = x, y = new, label = lab) +
#   #geom_point(aes(y = baseline), alpha = .5) + 
#   #geom_line() +
#   geom_segment(aes(yend = baseline, xend = x), alpha = .2, size = 20) + 
#   facet_wrap("outcome", nrow = 2) + 
#   geom_label() +  
#   geom_text(aes(label = lab2, y = baseline), check_overlap = T, hjust = 0, vjust = 1.5) +
#   labs(y = "Change in Level Provided\n(e.g., the number of requests to agencies)",
#        title = "") + 
#   theme_void() + 
#   xlim(-.1,1.1) + 
#   ylim(-1.1,3.1) + 
#   theme(panel.border = element_rect(fill=NA),
#         axis.title.y = element_text(angle = 90))

1x2

# just 
x2 %>% 
  filter(outcome %in% c("3. Only Increased Capacity Affects Behavior", "2. Only Shifting Priorities Affect Behavior")) %>% 
  mutate(outcome = str_remove(outcome, "[1-9].*Only ")) %>%
  ggplot() +
  aes(x = x, y = new, label = lab) +
  geom_line(aes(y = baseline), linetype = 2, alpha = .5) + 
  geom_line() +
  geom_ribbon(aes(ymax = new, ymin = baseline), alpha = .2) + 
  facet_wrap("outcome", nrow = 1) + 
  geom_label() +  
  geom_text(y = 0, 
            vjust = 1.1,
            label = "Baseline levels for a new legislator\n(may differ between service & policy work)",
            aes(x = ifelse(outcome == "Increased Capacity Affects Behavior", 0.5, NA))) +
  labs(y = "Change in Level Provided\n(e.g., the number of\nrequests to agencies)",
       title = "") + 
  theme_void() + 
  xlim(-.3,1.3) + 
  ylim(-1.1,1.1) + 
  theme(panel.border = element_rect(fill=NA),
        axis.title.y = element_text(angle = 90))

Divergent Predictions for the Change in Levels of Constituency Service and Policy Work as Legislators Gain Experience and Power

Data

FOIA <- read_csv(here("data/_FOIA_response_table.csv")) %>% select(-ends_with("1")) %>% 
  mutate(Department = Department %>% str_remove_all("Department of the|Department of ")) 

FOIA %>% 
  kablebox_long()

Department	Components FOIAed	Records received	Coded	Observations
Agriculture	29	29	11	9603
Commerce	19	18	10	7791
Defense	49	13	7	9806
Education	1	1	1	4676
Energy	8	2	1	6256
Health and Human Services	15	10	7	109701
Homeland Security	14	13	12	153151
Housing and Urban Development	2	1	1	32158
Justice	23	6	3	3096
Labor	22	12	9	62353
State	1	0	0	0
Transportation	10	7	6	26885
Veterans Affairs	6	3	2	90808
Interior	11	8	6	6067
Treasury	7	5	5	23853
Independent Agencies	77	47	30	81152
Total	294	175	111	627356

FOIA %>% select(-Coded) %>%
  rename(N = Observations) %>% 
  kable(format = "latex", booktabs = T) %>%
  row_spec(row = 16, hline_after = TRUE) %>%
  row_spec(row = 17, bold = T) %>% 
  #kable_styling(font_size = 9, full_width = TRUE) %>% 
       write_file(here::here("docs", "tables", "FOIA.tex"))

# can't cache data

# new raw data 
load(
  here::here("data", "all_contacts.Rdata") %>% 
       str_replace("Correspondence","correspondence_data")
     ) 

all_contacts %>% count() %>% pull(n)  %>% write(file = here::here("docs/tables/n"))

all_contacts %>% count(TYPE != "NA") %>% pull(n) %>% .[[2]] %>% write(file = here::here("docs/tables/n_coded"))

n_agencies <- all_contacts $agency %>% unique() %>% length()

write(n_agencies, file = here::here("docs", "tables", "n_agencies"))
# 
# # old raw data 
# load(
#   here::here("data", "all_contacts2.Rdata") 
#      ) 

all_contacts |> count(agency) |> kablebox()

agency	n
ABMC	78
Amtrak	1433
CNCS	836
CSOSA	34
DHHS_ACF	691
DHHS_ACL	112
DHHS_CDC	8578
DHHS_CMS	68555
DHHS_FDA	12495
DHHS_HRSA	10626
DHHS_IHS	1222
DHHS_NIH	7422
DHS_HQ	36622
DHS_ICE	642
DHS_USCIS	115887
DOC_EDA	925
DOC_IOS	339
DOC_MBDA	9
DOC_NIST	262
DOC_NOAA	50
DOC_NTIA	116
DOC_OCPA	52
DOC_OS	1486
DOC_SBA	4552
DOD_DFAS	688
DOD_DLA_Aviation	224
DOD_DeCA	531
DOD_Navy	4475
DOD_OIG	1654
DOD_OSDJS	1908
DOD_USACE	326
DOE_FERC	6256
DOI_BOEM	1398
DOI_BSEE	89
DOI_NIGC	6
DOI_NPS	4255
DOI_OSMRE	47
DOI_SOL	77
DOI_USGS	195
DOJ_CIV	1800
DOJ_ENRD	613
DOJ_EOIR	683
DOL_EBSA	10096
DOL_ETA	9000
DOL_MSHA	945
DOL_OASAM	397
DOL_OCFO	22
DOL_OFCCP	543
DOL_OSHA	2548
DOL_OWCP	10944
DOL_SOL	26862
DOL_VETS	996
DOT_FAA	12275
DOT_FHWA	9229
DOT_FRA	2705
DOT_FTA	2177
DOT_PHMSA	482
DOT_SLSDC	17
ED	4676
EEOC	823
EOP_CEQ	234
EOP_USTR	6998
EPA	10906
FCA	53
FCC	6068
FHFA	2849
FTC	12890
HUD_HQ	32158
NARA	9836
NASA	2777
NCPC	12
NCUA	351
NLRB	635
NWTRB	4
PRC	360
RRB	2325
SSA	4365
TVA	82
Treasury_Fiscal	11592
Treasury_IRS	10993
Treasury_OCC	1268
USDA	5006
USDA_ERS	42
USDA_FS	658
USDA_NASS	77
USDA_NIFA	121
USDA_NRCS	556
USDA_RD	2488
USDA_RMA	655
USPS	17203
VA	90788
VA_CEM	20

all_contacts |> count(year) |> kablebox()

year	n
2000	311
2001	812
2002	799
2003	686
2004	2783
2005	5522
2006	5204
2007	23376
2008	35731
2009	44956
2010	48743
2011	49610
2012	43545
2013	43491
2014	45727
2015	41503
2016	43352
2017	32911
2018	134375
2019	12896
2020	11023

all_contacts %>% filter(year >2006, year <2018) %>%  dim()

## [1] 452945    117

all_contacts2 <- all_contacts #FIXME 

#load(here::here("data", "all_contacts_paper.Rdata"))

all_contacts2 %<>% mutate(type = ifelse(TYPE %in% c(1,2,3), "Constituency\nService", NA) ) %>%
  mutate(type = ifelse(TYPE %in% c(4,5), "Policy\nWork", type) )

all_contacts2$Type %<>% str_replace("Indiv. Constituent" , "Constituent (Individual)") %>% 
  str_replace("Corp. Constituent" , "Constituent (Corporate)")%>% 
  str_replace("501c3 or Local Gov." , "Constituent (501c3 or Local Gov.)")%>% 
  str_replace("Corp. Policy" , "Policy (Corporate)")%>% 
  str_replace("^Policy$" , "Policy (General)")


load(here::here("data", "dcounts_min.Rdata"))
load(here::here("data", "dcounts_tenure.Rdata"))
load(here::here("data", "dcounts_ratio.Rdata"))
load(here::here("data", "dcounts_per_district.Rdata"))


load(here::here("data", "members.Rdata"))

members %<>% mutate(member_state = member_state %>%
                     str_replace("\\) ", "-") %>%
                     str_c(")")) 


congress_years2<- function(congress){
        years<- c(congress*2 + 1787, congress*2 + 1788 ) %>% str_c(collapse = ";", sep = ";")
        return(years)
}

# FIXME members data should have years so we don't merge in observations for members in the january (final days of their last term)
# members2 <- members %>%
# mutate(year = congress %>%  congress_years2() %>% str_c(collapse = ";"))

# CAUTION THIS IS WRONG AND WILL PRINT OUT ALL YEARS IN DATA ^2
# members2$year[1]

load(here::here('data', 'agency_vars.Rdata'))

congress_years<- function(congress){
        years<- c(congress*2 + 1787, congress*2 + 1788 )
        return(years)
}

year_congress<- function(year){
    return(floor((year - 1787)/2))
}



d <- dcounts_min %>% 
  ungroup() %>% 
  mutate(type = ifelse(TYPE %in% c(1,2,3), "Constituency\nService", NA) ) %>%
  mutate(type = ifelse(TYPE %in% c(4,5), "Policy\nWork", type) )

type <- d %>% 
  group_by(type) %>% 
  summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>% 
  ungroup()


type %>% 
  rename(type2 = type) %>%
  mutate(percent = n/sum(type$n[1:2])*100) %>% kablebox()

type2	n	percent
Constituency Service	43616 Work	8259

type5 <- d %>% 
  group_by(TYPE) %>% 
  summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>% 
  ungroup() 

type5 %<>% 
  mutate(percent = n/sum(type5$n[2:6])*100)

type5 %>% kablebox()

TYPE	n	percent
	3	0.0006599
0	5117	1.1255678
1	367633	80.8668874
2	30773	6.7690243
3	37763	8.3065891
4	13329	2.9319314
5	69267	15.2364088
6	2584	0.5683930
NA	100887	22.1917447

type2017 <- d %>% 
  filter(year> 2006, year < 2018) %>%
  group_by(type) %>% 
  summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>%
  ungroup()

type2020 <- d %>% 
  filter(year> 2006, year < 2021) %>%
  group_by(type) %>% 
  summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>%
  ungroup()

write(sum(type2020$n), file = "tables/n2007-2020")

coded <- d %>% 
    filter(year> 2006, year < 2021) %>%
  mutate(coded = !is.na(type)) %>% 
  group_by(coded) %>% 
  summarise(n = sum(per_icpsr_chamber_year_agency_type)) 

coded %>% filter(coded) %>% .$n |> write(file = "tables/n2007-2020coded")

coded_ratio = coded$n[2]/(sum(coded$n))

multiplier = sum(coded$n)/coded$n[2]

percent <- function(x){
  y <- x*100 
  y %<>% round() 
  return(y)}
# percent(.434534523453)

d %<>% filter(chamber != "President")

d %<>% mutate(congress = year_congress(year))




dcounts <- d %>% left_join(members)


type2xchair <- dcounts %>% 
  group_by(type, chair) %>% 
  summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>% 
  ungroup() %>% 
  drop_na(type, chair) %>% 
  group_by(chair) %>% 
  mutate(percent = n / sum(n) *100) 

type2xchair%>% 
  kablebox()

type	chair	n	percent
Constituency Service	Service	Work	Work

type2xprestigechair <- dcounts %>% 
  group_by(type, prestige_chair) %>% 
  summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>% 
  ungroup() %>% 
  drop_na() %>% 
  group_by(prestige_chair) %>% 
  mutate(percent = n / sum(n) *100) 

type2xprestigechair%>% 
  kablebox()

type	prestige_chair	n	percent
Constituency Service	Service	Work	Work

# prestige committee
type2xprestige <- dcounts %>% 
  group_by(type, prestige) %>% 
  summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>% 
  ungroup() %>% 
  drop_na() %>% 
  group_by(prestige) %>% 
  mutate(percent = n / sum(n) *100) 


type2xprestige%>% 
  kablebox()

type	prestige	n	percent
Constituency Service	Service	Work	Work

type2xchamber <- dcounts %>%
  group_by(type, chamber) %>% 
  summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>% 
  ungroup() %>% 
  drop_na() %>% 
  group_by(chamber) %>% 
  mutate(percent = n / sum(n) *100) 


# year
type2xyear <- dcounts %>% mutate(years = year  - yearelected) %>% 
  group_by(type, years) %>% 
  summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>% 
  ungroup() %>% 
  drop_na() %>% 
  group_by(years) %>% 
  mutate(percent = n / sum(n) *100) 

type2xyear %>% 
  kablebox()

type	years	n	percent
Constituency Service	Service	Work	Work

#  averages 
dcounts %>%
  group_by(year, bioname) %>% 
  summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>% 
  ungroup() %>% 
  arrange(-n) %>%
  summarise(mean_per_year = mean(n) %>% round(0)) %>% 
  kablebox()

mean_per_year
55

# chamber averages 
dcounts %>%
  group_by(chamber, year, bioname) %>% 
  summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>% 
  ungroup() %>% 
  arrange(-n) %>%
  group_by(chamber) %>% 
  summarise(mean_per_year = mean(n) %>% round(0)) %>% 
  kablebox()

chamber	mean_per_year
House	40
Senate	117

# per member 
dcounts %>% 
  group_by(bioname, year) %>% summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>% 
  ungroup() %>% 
  arrange(-n) %>%
  group_by(bioname) %>% 
  mutate(mean_per_year = mean(n) %>% round(0)) %>% 
  kablebox_long()

bioname	year	n	mean_per_year
DURBIN, Richard Joseph	2018	1571	242
RUBIO, Marco	2018	1570	377
KLOBUCHAR, Amy	2018	1479	256
MENG, Grace	2018	1469	216
CLARKE, Yvette Diane	2018	1382	128
SCHUMER, Charles Ellis (Chuck)	2018	1380	358
DIAZ-BALART, Mario	2018	1219	98
TOOMEY, Patrick Joseph	2018	1193	258
ISAKSON, Johnny	2018	1159	208
GILLIBRAND, Kirsten	2018	1142	375
PORTMAN, Robert Jones (Rob)	2018	1118	114
SHAHEEN, Jeanne	2018	1082	254
WARNER, Mark	2014	1039	426
VAN HOLLEN, Christopher	2018	1037	177
BROWN, Sherrod	2018	1018	213
NELSON, Clarence William (Bill)	2018	1004	370
WARREN, Elizabeth	2018	1002	218
CORNYN, John	2018	946	242
PASCRELL, William J., Jr.	2018	935	69
TILLIS, Thomas Roland (Thom)	2018	931	237
DONOVAN, Daniel M., Jr.	2018	871	244
JEFFRIES, Hakeem	2018	867	120
KAINE, Timothy Michael (Tim)	2018	865	245
BOOKER, Cory Anthony	2018	862	302
HARRIS, Kamala Devi	2018	861	238
WEBB, James H. (Jim)	2010	860	533
GUTIÉRREZ, Luis V.	2018	854	56
CASEY, Robert (Bob), Jr.	2018	848	294
MURPHY, Christopher	2018	822	107
CHAMBLISS, Saxby	2009	821	165
CARDIN, Benjamin Louis	2018	820	153
SERRANO, José E.	2018	818	50
VELÁZQUEZ, Nydia M.	2018	810	56
MEEKS, Gregory W.	2018	790	53
McCAIN, John Sidney, III	2015	785	258
CURBELO, Carlos	2018	782	240
SMITH, Tina	2018	772	209
CONNOLLY, Gerald E. (Gerry)	2018	760	125
SCHUMER, Charles Ellis (Chuck)	2012	745	358
BYRD, Robert Carlyle	2009	709	204
DUCKWORTH, Tammy	2018	706	157
CROWLEY, Joseph	2018	704	60
PETERS, Gary C.	2018	681	107
BYRD, Robert Carlyle	2008	679	204
NELSON, Clarence William (Bill)	2010	676	370
WYDEN, Ronald Lee	2018	676	118
MIKULSKI, Barbara Ann	2010	667	249
GRASSLEY, Charles Ernest	2010	663	252
COMSTOCK, Barbara J.	2018	656	349
SCHUMER, Charles Ellis (Chuck)	2010	656	358
BURR, Richard M.	2018	652	157
WARNER, Mark	2015	649	426
GILLIBRAND, Kirsten	2013	633	375
NELSON, Clarence William (Bill)	2009	623	370
McCAIN, John Sidney, III	2016	620	258
WEBB, James H. (Jim)	2008	620	533
WEBB, James H. (Jim)	2009	619	533
REED, John F. (Jack)	2018	615	108
ALEXANDER, Lamar	2018	607	159
CORKER, Robert (Bob)	2018	604	168
GRASSLEY, Charles Ernest	2011	603	252
MOULTON, Seth	2018	602	157
WATSON COLEMAN, Bonnie	2018	600	125
CORTEZ MASTO, Catherine Marie	2018	596	168
DAVIS, Rodney	2018	593	170
NELSON, Clarence William (Bill)	2011	591	370
GILLIBRAND, Kirsten	2012	587	375
NADLER, Jerrold Lewis	2018	584	49
SCHUMER, Charles Ellis (Chuck)	2009	582	358
SIRES, Albio	2018	571	55
MALONEY, Carolyn Bosher	2018	569	74
DURBIN, Richard Joseph	2009	566	242
LUGAR, Richard Green	2010	564	203
WOODALL, Rob	2018	563	94
BEYER, Donald Sternoff Jr.	2018	562	180
O’ROURKE, Beto	2018	562	172
SCHUMER, Charles Ellis (Chuck)	2016	562	358
NELSON, Clarence William (Bill)	2013	556	370
KHANNA, Rohit	2018	552	146
ESPAILLAT, Adriano J.	2018	551	151
LUGAR, Richard Green	2011	550	203
MIKULSKI, Barbara Ann	2013	550	249
GRAHAM, Lindsey O.	2018	547	148
STABENOW, Deborah Ann	2018	547	111
HASTINGS, Alcee Lamar	2018	546	61
MIKULSKI, Barbara Ann	2011	545	249
SCHNEIDER, Brad	2018	545	117
COOPER, James Hayes Shofner	2018	542	43
NELSON, Clarence William (Bill)	2012	539	370
LANKFORD, James	2018	534	102
MANCHIN, Joe, III	2011	527	281
MARKEY, Edward John	2018	526	72
GILLIBRAND, Kirsten	2011	521	375
ESHOO, Anna Georges	2018	519	58
SCHUMER, Charles Ellis (Chuck)	2011	517	358
GILLIBRAND, Kirsten	2014	512	375
DONNELLY, Joe	2018	510	92
WOLF, Frank Rudolph	2009	507	212
HATCH, Orrin Grant	2018	503	111
GOMEZ, Jimmy	2018	502	129

# per member HOUSE
dcounts %>% 
  filter(chamber == "House") %>% 
  group_by(bioname, year) %>% summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>% 
  ungroup() %>% 
  arrange(-n) %>%
  group_by(bioname) %>% 
  mutate(mean_per_year = mean(n) %>% round(0)) %>% 
  kablebox_long()

bioname	year	n	mean_per_year
MENG, Grace	2018	1469	216
CLARKE, Yvette Diane	2018	1382	128
DIAZ-BALART, Mario	2018	1219	98
PASCRELL, William J., Jr.	2018	935	69
DONOVAN, Daniel M., Jr.	2018	871	244
JEFFRIES, Hakeem	2018	867	120
GUTIÉRREZ, Luis V.	2018	854	56
SERRANO, José E.	2018	818	50
VELÁZQUEZ, Nydia M.	2018	810	56
MEEKS, Gregory W.	2018	790	53
CURBELO, Carlos	2018	782	240
CONNOLLY, Gerald E. (Gerry)	2018	760	125
CROWLEY, Joseph	2018	704	60
COMSTOCK, Barbara J.	2018	656	349
MOULTON, Seth	2018	602	157
WATSON COLEMAN, Bonnie	2018	600	125
DAVIS, Rodney	2018	593	170
NADLER, Jerrold Lewis	2018	584	49
SIRES, Albio	2018	571	55
MALONEY, Carolyn Bosher	2018	569	74
WOODALL, Rob	2018	563	94
BEYER, Donald Sternoff Jr.	2018	562	180
O’ROURKE, Beto	2018	562	172
KHANNA, Rohit	2018	552	146
ESPAILLAT, Adriano J.	2018	551	151
HASTINGS, Alcee Lamar	2018	546	61
SCHNEIDER, Brad	2018	545	117
COOPER, James Hayes Shofner	2018	542	43
ESHOO, Anna Georges	2018	519	58
WOLF, Frank Rudolph	2009	507	212
GOMEZ, Jimmy	2018	502	129
PINGREE, Chellie	2018	502	73
LOWEY, Nita M.	2018	498	52
WOLF, Frank Rudolph	2010	497	212
ROS-LEHTINEN, Ileana	2018	484	64
SOTO, Darren Michael	2018	484	235
ENGEL, Eliot Lance	2018	481	46
WOLF, Frank Rudolph	2011	480	212
VISCLOSKY, Peter	2018	470	113
LIEU, Ted	2018	456	106
PALLONE, Frank, Jr.	2018	447	91
SCHAKOWSKY, Janice D.	2018	443	62
BARR, Garland H. (Andy) IV	2018	437	111
SCHIFF, Adam	2018	430	53
FRELINGHUYSEN, Rodney P.	2018	427	69
GALLEGO, Ruben	2018	423	88
JOHNSON, Hank	2018	422	102
SUOZZI, Thomas	2018	416	134
LEWIS, John R.	2018	411	52
DELANEY, John	2018	402	113
WOLF, Frank Rudolph	2012	400	212
PELOSI, Nancy	2018	398	47
PRICE, David Eugene	2018	395	46
CAPUANO, Michael Everett	2018	393	37
ADAMS, Alma	2018	390	68
WOLF, Frank Rudolph	2008	388	212
SPEIER, Karen Lorraine Jacqueline (Jackie)	2018	382	56
PANETTA, James Varni	2018	381	120
SMITH, Christopher Henry	2018	380	63
JAYAPAL, Pramila	2018	370	119
KINZINGER, Adam	2018	370	56
McGOVERN, James P.	2018	368	44
CASTOR, Kathy	2018	362	85
CÁRDENAS, Tony	2018	357	76
WALTERS, Mimi	2018	356	118
LOUDERMILK, Barry D.	2018	353	127
COLLINS, Chris	2018	352	107
SINEMA, Kyrsten	2018	351	92
GRAVES, Tom	2018	349	84
LANCE, Leonard	2018	347	76
OLSON, Pete	2018	344	64
DOYLE, Michael F.	2018	342	44
GARRETT, Scott	2014	341	105
BROWNLEY, Julia	2018	337	109
COFFMAN, Mike	2018	337	69
McCAUL, Michael T.	2012	335	173
SHIMKUS, John M.	2010	334	97
LOFGREN, Zoe	2018	333	35
COMSTOCK, Barbara J.	2016	332	349
GARRETT, Scott	2016	332	105
McCAUL, Michael T.	2010	331	173
HIMES, James A.	2018	326	62
ZELDIN, Lee M	2018	326	88
SESTAK, Joe	2010	317	224
WITTMAN, Robert J.	2018	316	116
CURTIS, John R.	2018	314	82
KEATING, William R.	2018	313	66
McCAUL, Michael T.	2009	312	173
GRIJALVA, Raúl M.	2018	311	63
SHIMKUS, John M.	2009	310	97
YARMUTH, John	2018	309	63
DENT, Charles W.	2010	307	122
RODRIGUEZ, Ciro D.	2008	306	96
GOTTHEIMER, Josh S.	2018	304	112
GOODLATTE, Robert William	2014	302	124
RICE, Kathleen Maura	2018	302	127
CASTRO, Joaquin	2018	301	65
CHU, Judy	2018	301	62
PAYNE, Donald, Jr.	2018	300	45
EDWARDS, Donna F.	2009	299	97

# per member VA-10
dcounts %>% 
  filter(cqlabel == "(VA-10)") %>% 
  group_by(bioname, year) %>% summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>% 
  ungroup() %>% 
  arrange(year) %>%
  group_by(bioname) %>% 
  mutate(mean_per_year = mean(n) %>% round(0)) %>% 
  kablebox_long()

bioname	year	n	mean_per_year
WOLF, Frank Rudolph	2000	0	212
WOLF, Frank Rudolph	2001	1	212
WOLF, Frank Rudolph	2002	0	212
WOLF, Frank Rudolph	2003	4	212
WOLF, Frank Rudolph	2004	23	212
WOLF, Frank Rudolph	2005	47	212
WOLF, Frank Rudolph	2006	53	212
WOLF, Frank Rudolph	2007	238	212
WOLF, Frank Rudolph	2008	388	212
WOLF, Frank Rudolph	2009	507	212
WOLF, Frank Rudolph	2010	497	212
WOLF, Frank Rudolph	2011	480	212
WOLF, Frank Rudolph	2012	400	212
WOLF, Frank Rudolph	2013	280	212
WOLF, Frank Rudolph	2014	257	212
COMSTOCK, Barbara J.	2015	245	349
COMSTOCK, Barbara J.	2016	332	349
COMSTOCK, Barbara J.	2017	164	349
COMSTOCK, Barbara J.	2018	656	349
WEXTON, Jennifer	2019	36	40
WEXTON, Jennifer	2020	43	40

dcounts %>% 
  group_by(bioname, year) %>% summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>% 
  ungroup() %>% 
  arrange(n) %>%
  group_by(bioname) %>% 
  mutate(mean_per_year = mean(n) %>% round(0)) %>% 
  #FIXME 
  filter(n > 4) %>% 
  kablebox_long()

bioname	year	n	mean_per_year
ADERHOLT, Robert	2005	5	44
ADERHOLT, Robert	2006	5	44
ALEXANDER, Rodney	2006	5	99
ALLEN, George	2003	5	23
ALLEN, Thomas H.	2003	5	9
AMASH, Justin	2019	5	43
BACHUS, Spencer T., III	2006	5	34
BAIRD, Brian	2006	5	17
BAKER, Richard Hugh	2004	5	3
BALDACCI, John Elias	2001	5	3
BALDERSON, Troy	2019	5	14
BARTON, Joe Linus	2003	5	36
BARTON, Joe Linus	2004	5	36
BENNETT, Robert	2005	5	11
BENNETT, Robert	2006	5	11
BIDEN, Joseph Robinette, Jr.	2005	5	11
BIDEN, Joseph Robinette, Jr.	2009	5	11
BISHOP, Robert (Rob)	2007	5	24
BLACKBURN, Marsha	2005	5	36
BLUMENAUER, Earl	2004	5	25
BLUMENAUER, Earl	2006	5	25
BLUMENAUER, Earl	2019	5	25
BOEHLERT, Sherwood Louis	2004	5	4
BONAMICI, Suzanne	2019	5	41
BONILLA, Henry	2004	5	2
BONNER, Jr., Josiah Robins (Jo)	2006	5	68
BOUCHER, Frederick C.	2001	5	24
BOUCHER, Frederick C.	2006	5	24
BOXER, Barbara	2002	5	140
BRADY, Kevin Patrick	2020	5	32
BRADY, Robert A.	2002	5	26
BREAUX, John Berlinger	2001	5	9
BROOKS, Mo	2019	5	47
BROWN, Henry Edward, Jr.	2005	5	32
BROWN, Henry Edward, Jr.	2006	5	32
BROWN, Sherrod	2002	5	213
BROWN, Sherrod	2006	5	213
BURGESS, Michael C.	2006	5	41
BURNS, Conrad	2001	5	9
BURR, Richard M.	2002	5	157
BURR, Richard M.	2004	5	157
BURTON, Danny Lee	2006	5	23
CAMPBELL, John (of California)	2006	5	22
CAPITO, Shelley Moore	2006	5	46
CAPPS, Lois	2003	5	45
CAPUANO, Michael Everett	2004	5	37
CAPUANO, Michael Everett	2005	5	37
CARDIN, Benjamin Louis	2004	5	153
CARDIN, Benjamin Louis	2005	5	153
CARSON, André	2019	5	33
CASE, Ed	2006	5	5
CASTLE, Michael Newbold	2005	5	11
CHABOT, Steve	2019	5	19
CLAY, William Lacy, Jr.	2005	5	36
CLINTON, Hillary Rodham	2009	5	56
COLEMAN, Norm	2004	5	42
COOK, Paul	2019	5	40
COOK, Paul	2020	5	40
CORNYN, John	2003	5	242
CORZINE, Jon Stevens	2002	5	4
CUNNINGHAM, Joe	2020	5	7
CURTIS, John R.	2020	5	82
DAVIS, Jim	2005	5	1
DEAL, John Nathan	2004	5	15
DIAZ-BALART, Mario	2004	5	98
DICKS, Norman DeValois	2001	5	19
DICKS, Norman DeValois	2004	5	19
DINGELL, Debbie	2019	5	54
DODD, Christopher John	2002	5	58
DORGAN, Byron Leslie	2005	5	28
DUNN, Jennifer Blackburn	2001	5	3
DeFAZIO, Peter Anthony	2004	5	44
DeFAZIO, Peter Anthony	2005	5	44
DeLAURO, Rosa L.	2004	5	52
ESHOO, Anna Georges	2004	5	58
FARR, Sam	2001	5	31
FEINGOLD, Russell Dana	2002	5	92
FILNER, Bob	2001	5	66
FINKENAUER, Abby	2019	5	6
FLORES, Bill	2020	5	47
FRIST, William H.	2004	5	3
FRIST, William H.	2006	5	3
GALLEGLY, Elton W.	2006	5	21
GARAMENDI, John	2020	5	61
GERLACH, Jim	2005	5	60
GIANFORTE, Greg	2019	5	19
GIBBONS, James A.	2005	5	2
GILCHREST, Wayne Thomas	2006	5	9
GONZALEZ, Charles A.	2005	5	20
GRANGER, Kay	2005	5	26
GREGG, Judd Alan	2001	5	58
GRIJALVA, Raúl M.	2005	5	63
GUEST, Michael	2020	5	6
GUTIÉRREZ, Luis V.	2005	5	56
HAGEL, Charles Timothy (Chuck)	2004	5	15
HALL, Ralph Moody	2006	5	23
HASTERT, John Dennis	2005	5	3
HASTINGS, Richard Norman (Doc)	2002	5	24
HASTINGS, Richard Norman (Doc)	2004	5	24
HERGER, Walter William (Wally)	2002	5	42

Example

Rep. Barbara Comstock worked in Rep. Frank Wolf’s congressional office for five years, described him as a “longtime mentor,” and claimed that Wolf first urged her to run for office. He endorsed and campaigned with her.

Notice several patterns - dip in constituency service when Wolf is no longer running for re-election - further dip with transition - quick rebound as Comstock gets her office in order - inheriting a highly-effective office, Comstock is far above average house member

cq <- "(VA-10)"

dcounts %>% 
  group_by(member_state, year, chamber, cqlabel) %>% summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>% 
  ungroup() %>% 
  group_by(chamber) %>% 
  mutate(mean_per_chamber = mean(n) %>% round(0)) %>% 
  filter(cqlabel == cq, year > 2007, year <2017) %>% 
  ggplot() +
  aes(x = year, y = n, fill = member_state) + 
  geom_col(alpha = .5, position = "dodge") +
  geom_hline(aes(yintercept = mean_per_chamber), linetype = 2) + 
  geom_text(aes(y = mean_per_chamber), x = 2007.5, label = "House mean", min.segment.length = 0, size = 3, fill = NA, vjust = -.3, hjust = 0) + 
  scale_x_continuous(breaks = seq(2008, 2016, 1)) +
  labs(x = "",
       y = "Number of Requests", 
       fill = cq %>% str_remove_all("\\(|\\)")) + 
  theme_minimal() + 
  theme(panel.grid.major.x =  element_blank(),
        panel.grid.minor.x =  element_blank(),
        axis.text.x = element_text(angle = 45))+ 
    scale_fill_viridis_d(begin = 0, end = .6, option = "cividis", direction = -1)

cq <- "(NY-07)"
CHAMBER <- "House"
dcounts %>% 
  group_by(member_state, year, chamber, cqlabel) %>% summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>% 
  ungroup() %>% 
  group_by(chamber) %>% 
  mutate(mean_per_chamber = mean(n) %>% round(0)) %>% 
    filter(cqlabel == cq, year > 2007, year <2018) %>% 
  ggplot() +
  aes(x = year, y = n, 
      fill = member_state %>% str_replace("-.*", "\\)")) + 
  geom_col(alpha = .5, position = "dodge") +
  geom_hline(aes(yintercept = mean_per_chamber), linetype = 2) + 
  geom_text(aes(y = mean_per_chamber), x = 2007.5, label = "Chamber average", min.segment.length = 0, size = 3, fill = NA, vjust = 1.3, hjust = 0) + 
  scale_x_continuous(breaks = seq(2007, 2017, 1)) +
  labs(x = "",
       y = "Number of Requests", 
       fill = str_c(CHAMBER, "-", cq) %>% str_remove_all("\\(|\\)") ) + 
  theme_minimal() + 
  theme(panel.grid.major.x =  element_blank(),
        panel.grid.minor.x =  element_blank(),
        axis.text.x = element_text(angle = 45))+ 
    scale_fill_viridis_d(begin = 0, end = .6, option = "cividis", direction = -1)

cq <- "(NY-14)"
CHAMBER <- "House"
dcounts %>% 
  group_by(member_state, year, chamber, cqlabel) %>% summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>% 
  ungroup() %>% 
  group_by(chamber) %>% 
  mutate(mean_per_chamber = mean(n) %>% round(0)) %>% 
  filter(cqlabel == cq, year > 2007, year <2018) %>% 
  #drop_na(year) %>%
  ggplot() +
  aes(x = year, y = n, 
      fill = member_state %>% str_replace("-.*", "\\)")) + 
  geom_col(alpha = .5, position = "dodge") +
  geom_hline(aes(yintercept = mean_per_chamber), linetype = 2) + 
  geom_text(aes(y = mean_per_chamber), x = 2007.5, label = "Chamber average", min.segment.length = 0, size = 3, fill = NA, vjust = 1.3, hjust = 0) + 
  scale_x_continuous(breaks = seq(2008, 2017, 1)) +
  labs(x = "",
       y = "Number of Requests", 
       fill = str_c(CHAMBER, "-", cq) %>% str_remove_all("\\(|\\)") ) + 
  theme_minimal() + 
  theme(panel.grid.major.x =  element_blank(),
        panel.grid.minor.x =  element_blank(),
        axis.text.x = element_text(angle = 45))+ 
    scale_fill_viridis_d(begin = 0, end = .6, option = "cividis", direction = -1)

case_study <- function(cq){

  CHAMBER <- filter(members, cqlabel == cq) %>% 
    .$chamber %>% .[1]

dcounts %>% 
  group_by(member_state, year, chamber, cqlabel) %>% summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>% 
  ungroup() %>% 
  group_by(chamber) %>% 
  mutate(mean_per_chamber = mean(n) %>% round(0)) %>% 
  filter(cqlabel == cq, year > 2007, year <2017) %>% 
  ggplot() +
  aes(x = year, 
      y = n, 
      fill = member_state %>% str_replace("-.*", "\\)")) + 
  geom_col(alpha = .5, position = "dodge") +
  geom_hline(aes(yintercept = mean_per_chamber), linetype = 2) + 
  geom_text(aes(y = mean_per_chamber), x = 2007.5, label = "Chamber average",# min.segment.length = 0,
            size = 3, vjust = 1.3, hjust = 0) + 
  scale_x_continuous(breaks = seq(2008, 2016, 1)) +
  labs(x = "",
       y = "Number of Requests", 
       fill = str_c(CHAMBER, "-", cq) %>% str_remove_all("\\(|\\)") ) + 
  theme_minimal() + 
  theme(panel.grid.major.x =  element_blank(),
        panel.grid.minor.x =  element_blank(),
        axis.text.x = element_text(angle = 45))+ 
    scale_fill_viridis_d(begin = 0, end = .6, option = "cividis", direction = -1) 
  
ggsave(str_c("figs/districts/", cq, ".png"), 
       width = 5.3,
  height = 2.5,
  dpi = 1000)
} 

case_study("(WI)")

case_study("(WI-07)")

cases <- unique(members$cqlabel) #%>% head()

# map(cases, case_study )

Between February 2017 and February 2021, we received data on 627356 instances of members of Congress contacting federal agencies. We focus on requests made from 2007-2012, resulting in a data set of 452945 contacts from members of Congress with federal agencies.

Requests by percentile

dcounts %>% 
    group_by(member_state, chamber, year) %>% summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>%
  group_by(member_state, chamber) %>% 
  summarise(mean = mean(n)) %>% 
  ungroup() %>% 
    group_by(chamber) %>% 
  mutate(Percentile = ntile(mean, 100),
         rank = dense_rank(-mean)) %>% 
  arrange(-Percentile) %>% 
  dplyr::select(member_state, Percentile, mean, chamber,rank) %>% distinct() %>% 
    ggplot() + 
    geom_col(aes(x = Percentile, y = mean), 
             color = "grey", 
             width = .000001,
             fill = "black",
             position = "dodge") + 
  geom_point(aes(x = Percentile, y = mean), color = "light blue") + 
  geom_text(aes(x = Percentile, y = mean, label = ifelse(rank <14 | Percentile < 30, member_state, "")), check_overlap = T, 
                  size = 2,
                  hjust = "inward") + 
  theme_minimal() + 
  labs(#title = "Average Legislator Requests per Year by Percentile",
       x = "Percentile Within Chamber",
       y = "Average Requests per Year") +     facet_grid(. ~ chamber , scales = "free_y")

Requests by year

dcounts %<>% 
  group_by(bioname, year) %>% 
  mutate(per_bioname_year = sum(per_icpsr_chamber_year_agency_type)) %>%
  ungroup() %>% 
  group_by(year) %>%
  #mutate(per_bioname_year = per_bioname_year - mean(per_bioname_year)) %>% 
  ungroup() 

schumer2013 <- dcounts %>% filter(bioname == "SCHUMER, Charles Ellis (Chuck)",
                   year == 2013) %>% .$per_bioname_year %>% unique()

cruz2013 <- dcounts %>% filter(bioname == "CRUZ, Rafael Edward (Ted)",
                   year == 2013) %>% .$per_bioname_year %>% unique()

mcconnell2013 <- dcounts %>% filter(bioname == "McCONNELL, Addison Mitchell (Mitch)",
                   year == 2013) %>% .$per_bioname_year %>% unique()


p <- dcounts %>% 
  filter(year > 2006, year <2018) %>%
  distinct(bioname, per_bioname_year, year, chamber) %>% 
  filter(per_bioname_year > 0) %>% 
  ggplot() + 
  geom_line(aes(x = year, 
                y = per_bioname_year, 
                group = bioname, 
                color = chamber), 
            alpha = .1) +
  geom_line(aes(x = year, 
                group = bioname, 
                color = chamber,
                y = ifelse(bioname == "SCHUMER, Charles Ellis (Chuck)", per_bioname_year, NA))) + 
  geom_label(x = 2013, 
             y = schumer2013, 
             label = "Chuck Schumer") +
  geom_line(aes(x = year, group = bioname, color = chamber,
                y = ifelse(bioname == "CRUZ, Rafael Edward (Ted)", per_bioname_year, NA))) + 
  geom_label(x = 2013, 
             y = cruz2013, 
             label = "Ted Cruz") +
  geom_line(aes(x = year, group = bioname, color = chamber,
                y = ifelse(bioname == "McCONNELL, Addison Mitchell (Mitch)", per_bioname_year, NA))) + 
  geom_label(x = 2013, 
             y = mcconnell2013, 
             label = "Mitch McConnell") +
  labs(x = "",
       color = " ",
       y = paste("Requests per Legislator per Year"),
       title = "") +
  scale_x_continuous(breaks = unique(dcounts$year)) + 
  scale_color_viridis_d(begin = 0, end = .6, option = "cividis", direction = -1) +
  theme_minimal() + 
  theme(panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.text.x = element_text(angle = 30, vjust = 0)) 

# FIXME, this takes a long time 
p

ggsave(here::here("figs/counts-per-year.pdf"), p, height=3, width=6)

Requests by state population

library(scales)


dcounts %>% 
  filter(chamber == "Senate") %>% 
    group_by(member_state, year, pop2010) %>% 
  summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>% 
    group_by(member_state, pop2010) %>%
  summarise(mean = mean(n)) %>% ungroup() %>% 
    ggplot() + 
    geom_point(aes(x = pop2010, y = mean), color = "light blue") + 
    geom_smooth(aes(x = pop2010, y = mean)) + 
  geom_text_repel(aes(x = pop2010, 
                y = mean, 
                label = ifelse(mean > 260 |# & log(pop2010) < 17.4 | 
                                 mean < 80 & (log(pop2010) > 17 | log(pop2010) <14), 
                               member_state, 
                               "")), 
            seed = 44,
            check_overlap = T, 
            min.segment.length = 0,
            size = 2.5) + theme_bw() +
  labs(title = "",
       x = "State Population (log scale)",
       y = "Average Number of Requests per Year") +
  scale_x_log10(labels = comma)

Types of requests

84% of the average legislators’ contacts with agencies are about constituency service.

16% of the average legislators’ contacts with agencies are about policy work.

type	n
Constituency Service	43616 Work	8259

We classify legislator requests into five overall types:

All data:

# paper figure 
type_totals <- all_contacts2 %>% 
  filter(Type != "To be coded") %>%  
  mutate(total = n()) %>% 
  group_by(Type, total, type) %>% 
  summarise(nT = n()) %>% 
  mutate(percent = paste0(nT, " = ", 100*round(nT/total, 2), "%"),
         Type = str_c(Type, "\n    ", percent)) %>% 
  ungroup() %>% 
  group_by(type) %>% 
  mutate(nt = sum(nT),
         percent_t = paste0(100*round(nt/total, 2), "%"))

type_totals %<>% mutate(Type = Type %>% 
                          str_replace_all("\\)", "") %>% 
                          str_replace_all(" \\(", ",\n") )

# horizontal
type_totals %>% 
  ggplot() + 
  geom_col(alpha = .7,aes(x = Type, fill = type, y = nT) ) + 
  scale_y_continuous(breaks = seq(0,500000, 50000), limits = c(0,500000)) + 
  labs(x = "",
       fill = "",
       y = paste("Number of Contacts"),
       title = "") +
  #geom_text(aes(label = Type, x = Type,y = nT) ) + 
  theme(panel.border = element_blank(),
        panel.grid.major.x = element_blank(),
        axis.ticks = element_blank(),
        #axis.text.x.top = element_text(),
        legend.position ="none") + 
  ggpubr::geom_bracket(
    xmin = type_totals$Type[1], xmax = type_totals$Type[3], 
    y.position = 380000,
    label = paste(type_totals$percent_t[1], "Constituency Service" ),
    #hjust = 1,
    vjust = -1
  ) + 
  ggpubr::geom_bracket(
    xmin = type_totals$Type[4], xmax = type_totals$Type[5], 
    y.position = 85000,
    label = paste(type_totals$percent_t[5], "Policy Work"),
    #hjust = 1,
    vjust = -1
  )+ 
  scale_fill_viridis_d(option = "cividis", end = .8, direction = -1)

all_contacts2 %>% 
  filter(Type != "To be coded") %>%  
  mutate(total = n()) %>% 
  group_by(Type, total, type) %>% 
  summarise(nT = n()) %>% 
  mutate(percent = paste0(nT, " = ", 100*round(nT/total, 2), "%"),
         Type = str_c(Type, "\n    ", percent)) %>% 
  ungroup() %>% 
  #distinct(type, Type, nT)
  ggplot() + 
  geom_col(alpha = .7,aes(x = type, fill = Type, y = nT), position ="stack") + 
  #coord_flip() + 
  scale_y_continuous(labels = scales::comma) + 
  labs(x = "",
       fill = "",
       y = paste("Number of Contacts\nN =", sum(nrow(all_contacts2))),
       title = "") +
  #theme_void() + 
  theme(panel.border = element_blank(),
        panel.grid.major.x = element_blank(),
        axis.ticks = element_blank(),
        axis.text.x = element_text())

Horizontal format:

type_totals %<>% 
  #distinct(type, Type, nT)
  mutate(Type = Type %>% str_replace(",\n", ", ")) 

type_totals %>% 
  ggplot() + 
  geom_col(alpha = .7,aes(x = Type, fill = type, y = nT/1000) ) + 
  coord_flip() + 
  scale_y_continuous(breaks = seq(0,500, 100), limits = c(0,700)) + 
  labs(x = "",
       fill = "",
       y = paste("Number of Contacts (Thousands)"),
       title = "") +
  #theme_void() + 
  theme(panel.border = element_blank(),
        panel.grid.major.y = element_blank(),
        axis.ticks = element_blank(),
        axis.text.x.top = element_text(),
        legend.position ="none") + 
  ggpubr::geom_bracket(
    xmin = type_totals$Type[1], xmax = type_totals$Type[3], y.position = 400,
    label = paste0(type_totals$percent_t[1], "\n", type_totals$type[1]),
    hjust = -.3,
    vjust = 1.5,
    label.size = 3.5
  ) + 
  ggpubr::geom_bracket(
    xmin = type_totals$Type[4], xmax = type_totals$Type[5], y.position = 100,
    label = paste0(type_totals$percent_t[5], "\n", type_totals$type[5]),
    hjust = -.3,
    vjust = 1,
    label.size = 3.5
  )

all_contacts2 %>% 
  filter(Type != "To be coded") %>%  
  mutate(total = n()) %>% 
  group_by(Type, total, type) %>% 
  summarise(nT = n()) %>% 
  mutate(percent = paste0(nT, " = ", 100*round(nT/total, 2), "%"),
         Type = str_c(Type, "\n    ", percent)) %>% 
  ungroup() %>% 
  #distinct(type, Type, nT)
  ggplot() + 
  geom_col(alpha = .7,aes(x = type, fill = Type, y = nT/1000), position ="stack") + 
  coord_flip() + 
  scale_y_continuous(breaks = seq(0,500, 50)) + 
  labs(x = "",
       fill = "",
       y = paste("Number of Contacts (Thousands)"),
       title = "") +
  #theme_void() + 
  theme(panel.border = element_blank(),
        panel.grid.major.y = element_blank(),
        axis.ticks = element_blank(),
        axis.text.x.top = element_text())

2007-2017 data:

N <- all_contacts2 %>% 
  filter(year <2018,
         year > 2006) %>% 
  nrow()

all_contacts2 %>% 
  filter(Type != "To be coded",
         year <2018,
         year > 2006) %>%  
    mutate(total = n()) %>% 
  group_by(Type, total, type) %>% 
  summarise(nT = n()) %>% 
  mutate(percent = paste0(nT, " = ", 100*round(nT/total, 2), "%"),
         Type = str_c(Type, "\n    ", percent)) %>% 
  ungroup() %>% 
  #distinct(type, Type, nT)
  ggplot() + 
  geom_col(alpha = .7,aes(x = type, fill = Type, y = nT/1000), position ="stack") + 
  coord_flip() + 
  scale_y_continuous(breaks = seq(0,500, 50)) + 
  labs(x = "",
       fill = "",
       y = paste("Number of Contacts (Thousands)\nN =", N),
       title = "") +
  #theme_void() + 
  theme(panel.border = element_blank(),
        panel.grid.major.y = element_blank(),
        axis.ticks = element_blank(),
        axis.text.x.top = element_text())

Requests by institutional position

The first plot is the contacts we have coded. The second assumes these ratios hold for uncoded letters.

chairs <- all_contacts %>% distinct(icpsr, chair, year) 

d %<>% left_join(chairs) %>% 
  mutate(chair = chair %>%
           replace_na(0),
         Chair = ifelse(chair == 1, "Committee Chair", " Non-Chair"))

chair <- d %>% 
  group_by(type, year, icpsr, Chair) %>% 
  summarise(n = sum(per_icpsr_chamber_year_agency_type)) %>%
  group_by(type, Chair) %>% 
  summarise(mean = mean(n)) %>% 
  ungroup() %>%
  filter(!is.na(type))

chair %>% 
  ggplot()  + 
  aes(x = type, y = mean) +
  geom_col(alpha = .7,position = "dodge") + 
  facet_wrap("Chair") + 
  labs(x = "", # "Type of Legislator Request to Agency",
       y = "Average Requests per Year") +
  theme(panel.grid.major.x = element_blank(),
        panel.border = element_blank(),
        axis.ticks.x = element_blank())

Change in Levels of Constituency Service and Policy Work as Legislators Gain Power

chair %>% 
  # assuming consistant proportions to uncoded data 
  mutate(mean = mean*multiplier) %>% 
  ggplot()  + 
  aes(x = type, y = mean) +
  geom_col(alpha = .7,position = "dodge") + 
  facet_wrap("Chair") + 
  labs(x = "Type of Legislator Request to Agency",
       y = "Average Requests per Year") +
  theme(panel.grid.major.x = element_blank(),
        panel.border = element_blank(),
        axis.ticks.x = element_blank())

Change in Levels of Constituency Service and Policy Work as Legislators Gain Power

Level

Member-level

TODO

[ x ] members.Rdata going back to most senior member’s first Congress in “data/nominate.Rdata”
[ x ] what to do with party and chamber switchers? Drop them from counts.

#load(here::here('data', 'dcounts_min.Rdata'))

##creating the aggregate count variable
## one obs per agency, member, year 
d <- dcounts_min %>% 
  group_by(agency, icpsr, chamber, year) %>% 
  summarise(perYear = sum(per_icpsr_chamber_year_agency_type)) 

# dcounts_min %>% 
#   mutate(type2 = ifelse(TYPE %in% c(1,2,3), "policy", NA ),
#          type2 = ifelse(TYPE %in% c(4,5), "constituent", type2)) %>% 
#   group_by(type2, agency, icpsr, chamber, year) %>% 
#   # 2-type counts 
#   mutate(perYear_type2 = sum(per_icpsr_chamber_year_agency_type),
#          # versions with NAs for models
#          perYear_con = ifelse(type2 == "constituent", perYear_type2),
#          perYear_pol = ifelse(type2 == "policy", perYear_type2))
#   

# constituency service
d_sub <- dcounts_min %>%
  filter(TYPE %in% c(1, 2, 3)) %>%
  group_by(agency, icpsr, chamber, year) %>%
  summarise(perYear_con = sum(per_icpsr_chamber_year_agency_type))

# policy
d_sub2<- dcounts_min %>%
  subset(TYPE %in% c(4, 5)) %>%
  group_by(agency, icpsr, chamber, year) %>%
  summarise(perYear_pol = sum(per_icpsr_chamber_year_agency_type))


d %<>% left_join(d_sub) %>% left_join(d_sub2)

# congress
d$congress <- year_congress(d$year)

d %<>% left_join(members %>% 
                 select(congress, icpsr, party,party_code, state_abbrev, district_code, nominate.dim1, presidents_party, female, chair, ranking_minority, party_leader, party_whip, majority, prestige, prestige_chair, yearelected, state), by = c('congress', 'icpsr'))

#FIXME this should all be in members data 
# nom <- read_csv(here::here('data', 'nominate_data.csv'))
nom2<- members %>% 
  group_by(icpsr) %>% 
  summarise(first_cong = min(congress))

nom2$first_year <- 1787 + 2*nom2$first_cong

df<- left_join(d, nom2, by ='icpsr' )
#/FIXME 

# Tenure vars 
df$tenure<- df$year - df$first_year
df$first<- ifelse(df$tenure==0, 1, 0)
df$second<- ifelse(df$tenure==1, 1, 0)
df$third<- ifelse(df$tenure==2, 1, 0)
df$fourth<- ifelse(df$tenure==3, 1, 0)
df$fifth<- ifelse(df$tenure==4, 1, 0)
df$sixth<- ifelse(df$tenure==5, 1, 0)

# colnames(agency_vars)
df %<>% 
  #select(-oversight_committee, -oversight_committee_chair) %>% #FIXME these should be the same
  left_join(agency_vars %>% 
                    select(agency, icpsr, chamber, year, oversight_committee,
                           oversight_committee_chair), by = c('agency', 'icpsr', 'year', 'chamber')
            )

df$icpsr_agency<- paste(df$agency, df$icpsr, sep='_')
df$agency_year<- paste(df$agency, df$year, sep='_')

# tenure 
df %<>% ungroup() %>% 
  group_by(icpsr) %>% 
  mutate(max_year = max(tenure)) 

df$survive <- ifelse((df$chamber=='House' & df$max_year>1)| (df$chamber=='Senate' & df$max_year>5),1, 0 )


##purging repeated values 
#FIXME repeats are probably chamber switchers
df %<>% 
  add_count(icpsr, year, agency, chamber, 
            name = "n") 

#FIXME !!! 
doubles <- filter(df, n>1) # no doubles (doubles were chamber-switchers)

doubles %>% kablebox()

agency	icpsr	chamber	year	perYear	perYear_pol	congress	party_code	state_abbrev	district_code	nominate.dim1	presidents_party	female	ranking_minority	majority	prestige	yearelected	state	first_cong	first_year	tenure	third	fourth	fifth	sixth	oversight_committee	oversight_committee_chair	icpsr_agency	agency_year	max_year	survive	n
Amtrak	14435	House	2013	0	0	113	100	MA	0	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	16	0	0	0	0	NA	NA	Amtrak_14435	Amtrak_2013	23	1	2
Amtrak	14435	House	2013	0	0	113	100	MA	5	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	16	0	0	0	0	NA	NA	Amtrak_14435	Amtrak_2013	23	1	2
Amtrak	14435	House	2014	0	0	113	100	MA	0	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	17	0	0	0	0	NA	NA	Amtrak_14435	Amtrak_2014	23	1	2
Amtrak	14435	House	2014	0	0	113	100	MA	5	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	17	0	0	0	0	NA	NA	Amtrak_14435	Amtrak_2014	23	1	2
Amtrak	14435	Senate	2013	0	0	113	100	MA	0	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	16	0	0	0	0	NA	NA	Amtrak_14435	Amtrak_2013	23	1	2
Amtrak	14435	Senate	2013	0	0	113	100	MA	5	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	16	0	0	0	0	NA	NA	Amtrak_14435	Amtrak_2013	23	1	2
Amtrak	14435	Senate	2014	1	0	113	100	MA	0	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	17	0	0	0	0	0	0	Amtrak_14435	Amtrak_2014	23	1	2
Amtrak	14435	Senate	2014	1	0	113	100	MA	5	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	17	0	0	0	0	0	0	Amtrak_14435	Amtrak_2014	23	1	2
Amtrak	20115	House	2009	0	0	111	200	IL	0	0.274	0	0	0	NA	1	NA	illinois	107	2001	8	0	0	0	0	NA	NA	Amtrak_20115	Amtrak_2009	15	1	2
Amtrak	20115	House	2009	0	0	111	200	IL	10	0.274	0	0	0	0	1	2008	illinois	107	2001	8	0	0	0	0	NA	NA	Amtrak_20115	Amtrak_2009	15	1	2
Amtrak	20115	House	2010	1	0	111	200	IL	0	0.274	0	0	0	NA	1	NA	illinois	107	2001	9	0	0	0	0	0	0	Amtrak_20115	Amtrak_2010	15	1	2
Amtrak	20115	House	2010	1	0	111	200	IL	10	0.274	0	0	0	0	1	2008	illinois	107	2001	9	0	0	0	0	0	0	Amtrak_20115	Amtrak_2010	15	1	2
Amtrak	20115	Senate	2009	0	0	111	200	IL	0	0.274	0	0	0	NA	1	NA	illinois	107	2001	8	0	0	0	0	NA	NA	Amtrak_20115	Amtrak_2009	15	1	2
Amtrak	20115	Senate	2009	0	0	111	200	IL	10	0.274	0	0	0	0	1	2008	illinois	107	2001	8	0	0	0	0	NA	NA	Amtrak_20115	Amtrak_2009	15	1	2
Amtrak	20115	Senate	2010	0	0	111	200	IL	0	0.274	0	0	0	NA	1	NA	illinois	107	2001	9	0	0	0	0	NA	NA	Amtrak_20115	Amtrak_2010	15	1	2
Amtrak	20115	Senate	2010	0	0	111	200	IL	10	0.274	0	0	0	0	1	2008	illinois	107	2001	9	0	0	0	0	NA	NA	Amtrak_20115	Amtrak_2010	15	1	2
Amtrak	20730	House	2011	0	0	112	200	NV	0	0.463	0	0	0	0	1	2010	nevada	110	2007	4	0	0	1	0	NA	NA	Amtrak_20730	Amtrak_2011	11	1	2
Amtrak	20730	House	2011	0	0	112	200	NV	2	0.463	0	0	0	1	1	2010	nevada	110	2007	4	0	0	1	0	NA	NA	Amtrak_20730	Amtrak_2011	11	1	2
Amtrak	20730	House	2012	0	0	112	200	NV	0	0.463	0	0	0	0	1	2010	nevada	110	2007	5	0	0	0	1	NA	NA	Amtrak_20730	Amtrak_2012	11	1	2
Amtrak	20730	House	2012	0	0	112	200	NV	2	0.463	0	0	0	1	1	2010	nevada	110	2007	5	0	0	0	1	NA	NA	Amtrak_20730	Amtrak_2012	11	1	2
Amtrak	20730	Senate	2011	0	0	112	200	NV	0	0.463	0	0	0	0	1	2010	nevada	110	2007	4	0	0	1	0	NA	NA	Amtrak_20730	Amtrak_2011	11	1	2
Amtrak	20730	Senate	2011	0	0	112	200	NV	2	0.463	0	0	0	1	1	2010	nevada	110	2007	4	0	0	1	0	NA	NA	Amtrak_20730	Amtrak_2011	11	1	2
Amtrak	20730	Senate	2012	1	0	112	200	NV	0	0.463	0	0	0	0	1	2010	nevada	110	2007	5	0	0	0	1	0	0	Amtrak_20730	Amtrak_2012	11	1	2
Amtrak	20730	Senate	2012	1	0	112	200	NV	2	0.463	0	0	0	1	1	2010	nevada	110	2007	5	0	0	0	1	0	0	Amtrak_20730	Amtrak_2012	11	1	2
Amtrak	20735	House	2009	1	0	111	100	NY	0	-0.474	1	1	0	1	1	2008	new york	110	2007	2	1	0	0	0	0	0	Amtrak_20735	Amtrak_2009	13	1	2
Amtrak	20735	House	2009	1	0	111	100	NY	20	-0.474	1	1	0	1	1	2008	new york	110	2007	2	1	0	0	0	0	0	Amtrak_20735	Amtrak_2009	13	1	2
Amtrak	20735	House	2010	0	0	111	100	NY	0	-0.474	1	1	0	1	1	2008	new york	110	2007	3	0	1	0	0	NA	NA	Amtrak_20735	Amtrak_2010	13	1	2
Amtrak	20735	House	2010	0	0	111	100	NY	20	-0.474	1	1	0	1	1	2008	new york	110	2007	3	0	1	0	0	NA	NA	Amtrak_20735	Amtrak_2010	13	1	2
Amtrak	20735	Senate	2009	0	0	111	100	NY	0	-0.474	1	1	0	1	1	2008	new york	110	2007	2	1	0	0	0	NA	NA	Amtrak_20735	Amtrak_2009	13	1	2
Amtrak	20735	Senate	2009	0	0	111	100	NY	20	-0.474	1	1	0	1	1	2008	new york	110	2007	2	1	0	0	0	NA	NA	Amtrak_20735	Amtrak_2009	13	1	2
Amtrak	20735	Senate	2010	2	0	111	100	NY	0	-0.474	1	1	0	1	1	2008	new york	110	2007	3	0	1	0	0	0	0	Amtrak_20735	Amtrak_2010	13	1	2
Amtrak	20735	Senate	2010	2	0	111	100	NY	20	-0.474	1	1	0	1	1	2008	new york	110	2007	3	0	1	0	0	0	0	Amtrak_20735	Amtrak_2010	13	1	2
Amtrak	29534	House	2007	0	0	110	200	MS	0	0.376	1	NA	0	0	1	2006	mississippi	105	1997	10	0	0	0	0	NA	NA	Amtrak_29534	Amtrak_2007	23	1	2
Amtrak	29534	House	2007	0	0	110	200	MS	1	0.376	1	NA	0	0	1	2006	mississippi	105	1997	10	0	0	0	0	NA	NA	Amtrak_29534	Amtrak_2007	23	1	2
Amtrak	29534	House	2008	0	0	110	200	MS	0	0.376	1	NA	0	0	1	2006	mississippi	105	1997	11	0	0	0	0	NA	NA	Amtrak_29534	Amtrak_2008	23	1	2
Amtrak	29534	House	2008	0	0	110	200	MS	1	0.376	1	NA	0	0	1	2006	mississippi	105	1997	11	0	0	0	0	NA	NA	Amtrak_29534	Amtrak_2008	23	1	2
Amtrak	29534	Senate	2007	0	0	110	200	MS	0	0.376	1	NA	0	0	1	2006	mississippi	105	1997	10	0	0	0	0	NA	NA	Amtrak_29534	Amtrak_2007	23	1	2
Amtrak	29534	Senate	2007	0	0	110	200	MS	1	0.376	1	NA	0	0	1	2006	mississippi	105	1997	10	0	0	0	0	NA	NA	Amtrak_29534	Amtrak_2007	23	1	2
Amtrak	29534	Senate	2008	0	0	110	200	MS	0	0.376	1	NA	0	0	1	2006	mississippi	105	1997	11	0	0	0	0	NA	NA	Amtrak_29534	Amtrak_2008	23	1	2
Amtrak	29534	Senate	2008	0	0	110	200	MS	1	0.376	1	NA	0	0	1	2006	mississippi	105	1997	11	0	0	0	0	NA	NA	Amtrak_29534	Amtrak_2008	23	1	2
CNCS	14435	House	2013	1	0	113	100	MA	0	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	16	0	0	0	0	0	0	CNCS_14435	CNCS_2013	23	1	2
CNCS	14435	House	2013	1	0	113	100	MA	5	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	16	0	0	0	0	0	0	CNCS_14435	CNCS_2013	23	1	2
CNCS	14435	House	2014	0	0	113	100	MA	0	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	17	0	0	0	0	NA	NA	CNCS_14435	CNCS_2014	23	1	2
CNCS	14435	House	2014	0	0	113	100	MA	5	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	17	0	0	0	0	NA	NA	CNCS_14435	CNCS_2014	23	1	2
CNCS	14435	Senate	2013	0	0	113	100	MA	0	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	16	0	0	0	0	NA	NA	CNCS_14435	CNCS_2013	23	1	2
CNCS	14435	Senate	2013	0	0	113	100	MA	5	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	16	0	0	0	0	NA	NA	CNCS_14435	CNCS_2013	23	1	2
CNCS	14435	Senate	2014	0	0	113	100	MA	0	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	17	0	0	0	0	NA	NA	CNCS_14435	CNCS_2014	23	1	2
CNCS	14435	Senate	2014	0	0	113	100	MA	5	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	17	0	0	0	0	NA	NA	CNCS_14435	CNCS_2014	23	1	2
CNCS	20115	House	2009	0	0	111	200	IL	0	0.274	0	0	0	NA	1	NA	illinois	107	2001	8	0	0	0	0	NA	NA	CNCS_20115	CNCS_2009	15	1	2
CNCS	20115	House	2009	0	0	111	200	IL	10	0.274	0	0	0	0	1	2008	illinois	107	2001	8	0	0	0	0	NA	NA	CNCS_20115	CNCS_2009	15	1	2
CNCS	20115	House	2010	0	0	111	200	IL	0	0.274	0	0	0	NA	1	NA	illinois	107	2001	9	0	0	0	0	NA	NA	CNCS_20115	CNCS_2010	15	1	2
CNCS	20115	House	2010	0	0	111	200	IL	10	0.274	0	0	0	0	1	2008	illinois	107	2001	9	0	0	0	0	NA	NA	CNCS_20115	CNCS_2010	15	1	2
CNCS	20115	Senate	2009	0	0	111	200	IL	0	0.274	0	0	0	NA	1	NA	illinois	107	2001	8	0	0	0	0	NA	NA	CNCS_20115	CNCS_2009	15	1	2
CNCS	20115	Senate	2009	0	0	111	200	IL	10	0.274	0	0	0	0	1	2008	illinois	107	2001	8	0	0	0	0	NA	NA	CNCS_20115	CNCS_2009	15	1	2
CNCS	20115	Senate	2010	0	0	111	200	IL	0	0.274	0	0	0	NA	1	NA	illinois	107	2001	9	0	0	0	0	NA	NA	CNCS_20115	CNCS_2010	15	1	2
CNCS	20115	Senate	2010	0	0	111	200	IL	10	0.274	0	0	0	0	1	2008	illinois	107	2001	9	0	0	0	0	NA	NA	CNCS_20115	CNCS_2010	15	1	2
CNCS	20730	House	2011	0	0	112	200	NV	0	0.463	0	0	0	0	1	2010	nevada	110	2007	4	0	0	1	0	NA	NA	CNCS_20730	CNCS_2011	11	1	2
CNCS	20730	House	2011	0	0	112	200	NV	2	0.463	0	0	0	1	1	2010	nevada	110	2007	4	0	0	1	0	NA	NA	CNCS_20730	CNCS_2011	11	1	2
CNCS	20730	House	2012	0	0	112	200	NV	0	0.463	0	0	0	0	1	2010	nevada	110	2007	5	0	0	0	1	NA	NA	CNCS_20730	CNCS_2012	11	1	2
CNCS	20730	House	2012	0	0	112	200	NV	2	0.463	0	0	0	1	1	2010	nevada	110	2007	5	0	0	0	1	NA	NA	CNCS_20730	CNCS_2012	11	1	2
CNCS	20730	Senate	2011	0	0	112	200	NV	0	0.463	0	0	0	0	1	2010	nevada	110	2007	4	0	0	1	0	NA	NA	CNCS_20730	CNCS_2011	11	1	2
CNCS	20730	Senate	2011	0	0	112	200	NV	2	0.463	0	0	0	1	1	2010	nevada	110	2007	4	0	0	1	0	NA	NA	CNCS_20730	CNCS_2011	11	1	2
CNCS	20730	Senate	2012	0	0	112	200	NV	0	0.463	0	0	0	0	1	2010	nevada	110	2007	5	0	0	0	1	NA	NA	CNCS_20730	CNCS_2012	11	1	2
CNCS	20730	Senate	2012	0	0	112	200	NV	2	0.463	0	0	0	1	1	2010	nevada	110	2007	5	0	0	0	1	NA	NA	CNCS_20730	CNCS_2012	11	1	2
CNCS	20735	House	2009	0	0	111	100	NY	0	-0.474	1	1	0	1	1	2008	new york	110	2007	2	1	0	0	0	NA	NA	CNCS_20735	CNCS_2009	13	1	2
CNCS	20735	House	2009	0	0	111	100	NY	20	-0.474	1	1	0	1	1	2008	new york	110	2007	2	1	0	0	0	NA	NA	CNCS_20735	CNCS_2009	13	1	2
CNCS	20735	House	2010	0	0	111	100	NY	0	-0.474	1	1	0	1	1	2008	new york	110	2007	3	0	1	0	0	NA	NA	CNCS_20735	CNCS_2010	13	1	2
CNCS	20735	House	2010	0	0	111	100	NY	20	-0.474	1	1	0	1	1	2008	new york	110	2007	3	0	1	0	0	NA	NA	CNCS_20735	CNCS_2010	13	1	2
CNCS	20735	Senate	2009	0	0	111	100	NY	0	-0.474	1	1	0	1	1	2008	new york	110	2007	2	1	0	0	0	NA	NA	CNCS_20735	CNCS_2009	13	1	2
CNCS	20735	Senate	2009	0	0	111	100	NY	20	-0.474	1	1	0	1	1	2008	new york	110	2007	2	1	0	0	0	NA	NA	CNCS_20735	CNCS_2009	13	1	2
CNCS	20735	Senate	2010	4	0	111	100	NY	0	-0.474	1	1	0	1	1	2008	new york	110	2007	3	0	1	0	0	0	0	CNCS_20735	CNCS_2010	13	1	2
CNCS	20735	Senate	2010	4	0	111	100	NY	20	-0.474	1	1	0	1	1	2008	new york	110	2007	3	0	1	0	0	0	0	CNCS_20735	CNCS_2010	13	1	2
CSOSA	14435	House	2013	0	0	113	100	MA	0	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	16	0	0	0	0	NA	NA	CSOSA_14435	CSOSA_2013	23	1	2
CSOSA	14435	House	2013	0	0	113	100	MA	5	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	16	0	0	0	0	NA	NA	CSOSA_14435	CSOSA_2013	23	1	2
CSOSA	14435	House	2014	0	0	113	100	MA	0	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	17	0	0	0	0	NA	NA	CSOSA_14435	CSOSA_2014	23	1	2
CSOSA	14435	House	2014	0	0	113	100	MA	5	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	17	0	0	0	0	NA	NA	CSOSA_14435	CSOSA_2014	23	1	2
CSOSA	14435	Senate	2013	0	0	113	100	MA	0	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	16	0	0	0	0	NA	NA	CSOSA_14435	CSOSA_2013	23	1	2
CSOSA	14435	Senate	2013	0	0	113	100	MA	5	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	16	0	0	0	0	NA	NA	CSOSA_14435	CSOSA_2013	23	1	2
CSOSA	14435	Senate	2014	0	0	113	100	MA	0	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	17	0	0	0	0	NA	NA	CSOSA_14435	CSOSA_2014	23	1	2
CSOSA	14435	Senate	2014	0	0	113	100	MA	5	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	17	0	0	0	0	NA	NA	CSOSA_14435	CSOSA_2014	23	1	2
CSOSA	20730	House	2011	0	0	112	200	NV	0	0.463	0	0	0	0	1	2010	nevada	110	2007	4	0	0	1	0	NA	NA	CSOSA_20730	CSOSA_2011	11	1	2
CSOSA	20730	House	2011	0	0	112	200	NV	2	0.463	0	0	0	1	1	2010	nevada	110	2007	4	0	0	1	0	NA	NA	CSOSA_20730	CSOSA_2011	11	1	2
CSOSA	20730	House	2012	0	0	112	200	NV	0	0.463	0	0	0	0	1	2010	nevada	110	2007	5	0	0	0	1	NA	NA	CSOSA_20730	CSOSA_2012	11	1	2
CSOSA	20730	House	2012	0	0	112	200	NV	2	0.463	0	0	0	1	1	2010	nevada	110	2007	5	0	0	0	1	NA	NA	CSOSA_20730	CSOSA_2012	11	1	2
CSOSA	20730	Senate	2011	0	0	112	200	NV	0	0.463	0	0	0	0	1	2010	nevada	110	2007	4	0	0	1	0	NA	NA	CSOSA_20730	CSOSA_2011	11	1	2
CSOSA	20730	Senate	2011	0	0	112	200	NV	2	0.463	0	0	0	1	1	2010	nevada	110	2007	4	0	0	1	0	NA	NA	CSOSA_20730	CSOSA_2011	11	1	2
CSOSA	20730	Senate	2012	0	0	112	200	NV	0	0.463	0	0	0	0	1	2010	nevada	110	2007	5	0	0	0	1	NA	NA	CSOSA_20730	CSOSA_2012	11	1	2
CSOSA	20730	Senate	2012	0	0	112	200	NV	2	0.463	0	0	0	1	1	2010	nevada	110	2007	5	0	0	0	1	NA	NA	CSOSA_20730	CSOSA_2012	11	1	2
CSOSA	29534	House	2008	0	0	110	200	MS	0	0.376	1	NA	0	0	1	2006	mississippi	105	1997	11	0	0	0	0	NA	NA	CSOSA_29534	CSOSA_2008	23	1	2
CSOSA	29534	House	2008	0	0	110	200	MS	1	0.376	1	NA	0	0	1	2006	mississippi	105	1997	11	0	0	0	0	NA	NA	CSOSA_29534	CSOSA_2008	23	1	2
CSOSA	29534	Senate	2008	0	0	110	200	MS	0	0.376	1	NA	0	0	1	2006	mississippi	105	1997	11	0	0	0	0	NA	NA	CSOSA_29534	CSOSA_2008	23	1	2
CSOSA	29534	Senate	2008	0	0	110	200	MS	1	0.376	1	NA	0	0	1	2006	mississippi	105	1997	11	0	0	0	0	NA	NA	CSOSA_29534	CSOSA_2008	23	1	2
DHHS_ACF	14435	House	2013	0	0	113	100	MA	0	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	16	0	0	0	0	NA	NA	DHHS_ACF_14435	DHHS_ACF_2013	23	1	2
DHHS_ACF	14435	House	2013	0	0	113	100	MA	5	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	16	0	0	0	0	NA	NA	DHHS_ACF_14435	DHHS_ACF_2013	23	1	2
DHHS_ACF	14435	House	2014	0	0	113	100	MA	0	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	17	0	0	0	0	NA	NA	DHHS_ACF_14435	DHHS_ACF_2014	23	1	2
DHHS_ACF	14435	House	2014	0	0	113	100	MA	5	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	17	0	0	0	0	NA	NA	DHHS_ACF_14435	DHHS_ACF_2014	23	1	2
DHHS_ACF	14435	Senate	2013	0	0	113	100	MA	0	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	16	0	0	0	0	NA	NA	DHHS_ACF_14435	DHHS_ACF_2013	23	1	2
DHHS_ACF	14435	Senate	2013	0	0	113	100	MA	5	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	16	0	0	0	0	NA	NA	DHHS_ACF_14435	DHHS_ACF_2013	23	1	2
DHHS_ACF	14435	Senate	2014	1	1	113	100	MA	0	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	17	0	0	0	0	0	0	DHHS_ACF_14435	DHHS_ACF_2014	23	1	2
DHHS_ACF	14435	Senate	2014	1	1	113	100	MA	5	-0.511	1	0	1	0	1	2012	massachusetts	105	1997	17	0	0	0	0	0	0	DHHS_ACF_14435	DHHS_ACF_2014	23	1	2

# drop doubles 
df %<>% filter(n<2) 


# LEVEL MODELS

df %<>% 
  # mutate(agency_year) %>% 
  mutate(lpyear = log(perYear + 1))

District-level

Were most maps in place by 2011?

####
# Redistricting? were most maps in place by 2011?
decade<- case_when(df$year < 2011 ~ '0', 
                   df$year > 2010 ~ '1')

state_dist<- case_when(df$chamber=='Senate'~ paste(df$state,
                                                    df$district_code, 
                                                    sep='_' ), 
                       df$chamber =='House'~ paste(paste(df$state, 
                                                          df$district_code, 
                                                          sep='_'), 
                                                    decade,
                                                    sep='_'))




df$state_dist<- state_dist

perDist<- df %>% 
  group_by(year, state_dist, icpsr, chamber, tenure, state) %>% 
  summarise(perYear = sum(perYear)) %>%
  distinct()



## Now, we want to identify when there is a new member in a district.  To do this, we look for changes in the icpsr number
perDist$new_member<- ifelse(perDist$tenure==0, 1, 0)

perDist$second_year<- ifelse(perDist$tenure==1, 1, 0)
perDist$third_year<- ifelse(perDist$tenure==2, 1, 0)
perDist$fourth_year<- ifelse(perDist$tenure==3, 1, 0)
perDist$fifth_year<- ifelse(perDist$tenure==4, 1, 0)
perDist$sixth_year<- ifelse(perDist$tenure==5, 1, 0)


state_level<- perDist %>% group_by(state, year) %>% 
  summarise(numpers = n(), 
            sumYear = sum(perYear), 
            mean_new = mean(new_member)) 

state_new <- ifelse(state_level$mean_new>0, 1, 0)

state_level$state_new <- state_new

perDist %<>% left_join(state_level %>% 
                         select(state_new, mean_new, state, year), 
                    by = c('state', 'year'))
#########################
# saved as DistrictLevel
########################
# save(perDist, file =  here::here("data", "perDist.Rdata"))
############################################

TODO

Priority

##creating the ratio variable 
d_rat <- df %>% 
  group_by(year, icpsr, chamber,
           prestige, prestige_chair, chair, ranking_minority, majority, presidents_party,
           first, second, third, fourth, fifth, sixth) %>% 
  # per member per year counts per type2
  summarise(perCon = sum(perYear_con), 
            perPol = sum(perYear_pol)) %>% 
  # ratio of con to pol 
  #FIXME switch? 
  mutate(ratio = perCon/(perCon + perPol))

# drop doubles
d_rat %<>% group_by(year, icpsr, chamber) %>% add_count(name = "n")

doubles_ratio <- filter(d_rat, n>1)

doubles_ratio %>% kablebox()

year	icpsr	chamber	prestige	prestige_chair	chair	ranking_minority	majority	presidents_party	first	second	third	fourth	fifth	sixth	perCon	perPol	ratio	n
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
—-:	—–:	:——-	——–:	————–:	—–:	—————-:	——–:	—————-:	—–:	——:	—–:	——:	—–:	—–:	——:	——:	—–:	–:

d_rat %<>% filter(n<2) 

############################################
# d_rat saved as ProportionContact.dta')
# save(d_rat, file =  here::here("data", "d_rat.Rdata"))
############################################

# RATIO MODEL

TODO

Joint Effect of Capacity and Priority

We observe the total requests, \(z\), and ratio, \(r\), of policy work to constituency service. \(z\) is the sum of policy requests \(x\) and constituency service requests \(y\). \(r\) is \(x/y\)./

r = .2

r2 = .31

z = 100

x = z*r
y = z-z*r


z2 = 120

x2 = z2*r2
y2 = z2-z2*r2

Combining model estimates for the shift in the level of requests and relative priority of policy work to constituency service, we see that even as the ratio of policy work increases, constituency service also significantly increases.

These estimates are calculated by adding diff-in-diff regression coefficients for committee chairs’ increased levels of requests to the baseline levels for non-chairs. We also adjust the ratio of policy work to constituency service based on the diff-in-diff regression coefficients for committee chairs’ increased policy work ratio.

#FIXME replace with tidy model output 
# TABLE 1
level <- tribble(~term, ~estimate, ~std.error,
                 "chair", 0.206, 0.0951)

# per member across all agencies 
level$estimate <- level$estimate*n_agencies
level$std.error <- level$std.error*n_agencies

# ratio of policy work for non-chairs
base_ratio <- chair$mean[3]/chair$mean[1]

# level estimates with baseline ratio (for presenting level effects only)
level_con <- level %>% mutate(estimate = estimate*(1-base_ratio),
                               std.error = std.error*(1-base_ratio))

level_pol <- level %>% mutate(estimate = estimate*(base_ratio),
                               std.error = std.error*(base_ratio))


# Priority Table 
priority <- tribble(~term, ~estimate, ~std.error,
                 "chair", 0.0875, 0.0175)

# adjust level estimates with new ratio (joint effects )
new_ratio <- base_ratio + priority$estimate[1]

level_con <- level %>% mutate(estimate = estimate*(1-new_ratio),
                               std.error = std.error*(1-new_ratio))

level_pol <- level %>% mutate(estimate = estimate*(new_ratio),
                               std.error = std.error*(new_ratio))


#FIXME, include priority model uncertianty too
chair$std.error <- NA
# constituent error 
chair$std.error[2] <- level_con$std.error[1]
# policy error
chair$std.error[4] <- level_pol$std.error[1]


# chair mean = non-chair mean * level coef
# constituent estimate 
chair$mean[2] <- chair$mean[1] + (1 + level_con$estimate[1] )
# policy estimate
chair$mean[4] <- chair$mean[3] + (1+level_pol$estimate[1] )

chair_adjusted <- chair %>% 
  # assuming consistant proportions to uncoded data 
  mutate(mean = mean*multiplier) %>%
  # baseline = non-chairs mean
  mutate(baseline = ifelse(Chair == "Committee Chair", NA, mean) ) %>%
  fill(baseline) %>%
  # point estimates and uncertiatty only for chair estimates 
  mutate(estimate = ifelse(Chair == "Committee Chair", mean, NA)) %>% 
  mutate(std.error = ifelse(Chair == "Committee Chair", std.error, NA)) 

chair_adjusted %>% 
  #mutate(Chair = ifelse() )
  ggplot()  + 
  aes(x = type, y = mean) +
  geom_col(alpha = .7, position = "dodge") + 
  geom_hline(aes(yintercept = baseline), linetype = 2) + 
  geom_pointrange(aes(y = estimate,
                      ymin = mean-1.96*std.error,
                      ymax = mean+1.96*std.error)) + 
  facet_wrap("Chair") + 
  labs(x = "", #Type of Legislator Request to Agency",
       y = "Average Requests per Year") +
  theme(panel.grid.major.x = element_blank(),
        panel.border = element_blank(),
        axis.ticks.x = element_blank())

Difference-in-Difference Estimates of Change in Levels of Constituency Service and Policy Work as Legislators Gain Power

chair_adjusted %<>% mutate(baseline = ifelse(type =="Constituency\nService", baseline+ 12.4, baseline))


# stacked to show ratio with PERCENTS 
chair_adjusted %>% 
  #mutate(Chair = ifelse() )
  ggplot()  + 
  aes(x = Chair, y = mean, fill = type) +
  geom_col(alpha = .7, position = "fill") +  
  facet_wrap("Chair", scales = "free_x") + 
  labs(x = "", #Type of Legislator Request to Agency",
       y = "Share of Requests per Year",
       fill = "") +
  theme(panel.grid.major.x = element_blank(),
        axis.text.x = element_blank(),
        panel.border = element_blank(),
        axis.ticks.x = element_blank())

Difference-in-Difference Estimates of Change in Levels of Constituency Service and Policy Work as Legislators Gain Power

# stacked to show ratio + free scales 
chair_adjusted %>% 
  #mutate(Chair = ifelse() )
  ggplot()  + 
  aes(x = Chair, y = mean, fill = type) +
  geom_col(alpha = .7, position = "stack") + 
  #geom_hline(aes(yintercept = baseline), linetype = 2) + 
  #geom_pointrange(aes(y = estimate,
                      #ymin = mean-1.96*std.error,
                      #ymax = mean+1.96*std.error)) + 
  facet_wrap("Chair", scales = "free") + 
  #scale_y_
  labs(x = "", #Type of Legislator Request to Agency",
       y = "Average Requests per Year",
       fill = "") +
  theme(panel.grid.major.x = element_blank(),
        axis.text.x = element_blank(),
        panel.border = element_blank(),
        axis.ticks.x = element_blank())

Difference-in-Difference Estimates of Change in Levels of Constituency Service and Policy Work as Legislators Gain Power

# stacked to show ratio + CI
chair_adjusted %>% 
  #mutate(Chair = ifelse() )
  ggplot()  + 
  aes(x = Chair, y = mean, fill = type) +
  geom_col(alpha = .7, position = "stack") + 
  geom_hline(aes(yintercept = baseline), linetype = 2) + 
  geom_pointrange(aes(y = estimate,
                      ymin = mean-1.96*std.error,
                      ymax = mean+1.96*std.error)) + 
  facet_wrap("Chair", scales = "free") + 
  labs(x = "", #Type of Legislator Request to Agency",
       y = "Average Requests per Year",
       fill = "") +
  theme(panel.grid.major.x = element_blank(),
        axis.text.x = element_blank(),
        panel.border = element_blank(),
        axis.ticks.x = element_blank())

Difference-in-Difference Estimates of Change in Levels of Constituency Service and Policy Work as Legislators Gain Power

The baseline level (non-chairs) is 75.4521884 constituency service requests and 13.0828967 policy requests per year.

The baseline ratio (for non-chairs) of policy work to constituency service is 0.1733932. Adding the model coefficient from the difference in difference model of shifting priorities yields a new ratio of 0.2608932.

The model-estimated level for committee chairs is 93.4026395 constituency service requests and 20.1930186 policy requests per year.

#TODO

intercoder agrement for USTR
y-axis label and remove the title from inequality figure
facet distribution across types by policy and constituent, probably coord flip as well
[ x ] remove the title from state pop figure

Appendix

Unequal Representation: Evidence from a Census of Legislator Contacts with US Federal Agencies

Theory

2x2

1x2

Data

Example

Requests by percentile

Requests by year

Requests by state population

Types of requests

Requests by institutional position

Level

Member-level

District-level

Priority

Joint Effect of Capacity and Priority